No description has been provided for this image

A COSPONSORSHIP STORY¶

In this example we will take a look to a network that represents cosponsorship, let me show you a net from the period "July 1995-July 1996".

Let's fetch the data first:

In [1]:
# fetch the file (this will appear in your main folder after running the code)
# just do it once!
!wget 'https://github.com/Magallanes-at-UTDT/DataViz_shortTalk_3/raw/main/cospon_9596.gexf'

--2024-07-29 18:28:30--  https://github.com/Magallanes-at-UTDT/DataViz_shortTalk_3/raw/main/cospon_9596.gexf
Resolving github.com (github.com)... 20.201.28.151
Connecting to github.com (github.com)|20.201.28.151|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/Magallanes-at-UTDT/DataViz_shortTalk_3/main/cospon_9596.gexf [following]
--2024-07-29 18:28:31--  https://raw.githubusercontent.com/Magallanes-at-UTDT/DataViz_shortTalk_3/main/cospon_9596.gexf
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 308578 (301K) [text/plain]
Saving to: ‘cospon_9596.gexf’

cospon_9596.gexf    100%[===================>] 301.35K  --.-KB/s    in 0.01s   

2024-07-29 18:28:31 (22.0 MB/s) - ‘cospon_9596.gexf’ saved [308578/308578]

Now, let's read the file and show the relationships

In [2]:
import networkx as nx

# read in the data
net95_1=nx.read_gexf("cospon_9596.gexf")


# show net adjacency as a pandas data frame
nx.to_pandas_adjacency(net95_1)
Out[2]:
00046218 07238522 08201401 05847587 32785850 19833343 00013944 07960843 09179807 21082125 ... 08260934 16437772 07799019 10315984 07812880 07586258 07194374 08772985 29233392 17906056
00046218 0.0 2.0 1.0 4.0 1.0 1.0 5.0 1.0 1.0 3.0 ... 3.0 4.0 0.0 2.0 0.0 2.0 0.0 0.0 3.0 2.0
07238522 2.0 0.0 2.0 1.0 2.0 0.0 1.0 0.0 0.0 0.0 ... 2.0 1.0 0.0 0.0 8.0 2.0 0.0 0.0 1.0 0.0
08201401 1.0 2.0 0.0 2.0 2.0 0.0 1.0 0.0 0.0 0.0 ... 1.0 1.0 2.0 1.0 0.0 3.0 2.0 0.0 1.0 0.0
05847587 4.0 1.0 2.0 0.0 1.0 1.0 3.0 1.0 0.0 0.0 ... 1.0 5.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0
32785850 1.0 2.0 2.0 1.0 0.0 1.0 2.0 0.0 0.0 0.0 ... 1.0 1.0 0.0 0.0 0.0 3.0 3.0 0.0 4.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
07586258 2.0 2.0 3.0 1.0 3.0 0.0 1.0 0.0 0.0 1.0 ... 1.0 2.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0
07194374 0.0 0.0 2.0 0.0 3.0 1.0 1.0 0.0 1.0 0.0 ... 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 5.0 0.0
08772985 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
29233392 3.0 1.0 1.0 1.0 4.0 1.0 4.0 0.0 1.0 1.0 ... 1.0 2.0 0.0 1.0 0.0 1.0 5.0 0.0 0.0 0.0
17906056 2.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 ... 0.0 3.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0

120 rows × 120 columns

The adjacency tells us how many times TWO legislators appear as supporters of a bill proposal. The rows and columns names represent the legislator's code. Zero means they never supported a bill together during the time the information was collected (notice the zeroes in the diagonal). The existence of value greater than zero means there is a link between two legislators, and the value is the weight of that relationship.

Besides links (or edges) there are nodes, which represent the legislators. We have some attributes in those nodes:

In [3]:
import pandas as pd

pd.DataFrame.from_dict(net95_1.nodes, orient='index')
Out[3]:
votes weightCurrentGroup region counter experience experience_true votesPerc capital currentGroup party startGroup weightStartGroup label
00046218 7975.0 0.558 NA 12 0.0 0 0.002906 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 JHONG JUNCHAYA JUAN JOSE
07238522 8304.0 0.142 NA 25 0.0 0 0.003026 0 UNION POR EL PERU UNION POR EL PERU UNION POR EL PERU 0.141667 GUERRA GARCIA CUEVA ROGER ANTENOR
08201401 7791.0 0.058 NA 85 2.0 1 0.002839 0 COORDINADORA DEMOCRATICA-ACCION POPULAR ACCION POPULAR ACCION POPULAR 0.033333 ALVA ORLANDINI JAVIER ERNESTO
05847587 8436.0 0.558 NA 23 0.0 0 0.003074 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 CAMPOS BACA LUIS ESEQUIEL
32785850 6866.0 0.058 NA 50 1.0 1 0.002502 0 COORDINADORA DEMOCRATICA-ACCION POPULAR CODE - PAIS POSIBLE PAIS POSIBLE 0.041667 CHU RUBIO LUIS ALBERTO
... ... ... ... ... ... ... ... ... ... ... ... ... ...
07586258 6204.0 0.050 NA 17 0.0 0 0.002261 0 GRUPO PARLAMENTARIO PERU MOVIMIENTO CIVICO NACIONAL OBRAS MOVIMIENTO CIVICO NACIONAL OBRAS 0.016667 LAJO LAZO MANUEL CONSTANTINO
07194374 7354.0 0.558 NA 23 1.0 1 0.002680 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 AMURUZ GALLEGOS ROGER
08772985 12158.0 0.050 NA 21 4.0 1 0.004430 0 PARTIDO POPULAR CRISTIANO-RENOVACION RENOVACION RENOVACION 0.025000 CHIRINOS SOTO ENRIQUE
29233392 15353.0 0.558 NA 13 0.0 0 0.005595 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 FERNANDEZ BUSTINZA VICTOR RAUL
17906056 11490.0 0.558 NA 23 2.0 1 0.004187 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 BARRETO ESTRADA GAMALIEL

120 rows × 13 columns

Relevant nodes
¶

Let's compute some important measures that seek to identify actors whose relationship patterns may make them relevant in the network:

In [4]:
# relevant to connect groups
nx.set_node_attributes(net95_1, nx.betweenness_centrality(net95_1), "betweenness")
# relevant to spread information
nx.set_node_attributes(net95_1, nx.closeness_centrality(net95_1), "closeness")
# connected to well connected nodes
nx.set_node_attributes(net95_1, nx.eigenvector_centrality(net95_1), "eigenvector")

You see those values as new columns here:

In [5]:
net95_1_NodeData=pd.DataFrame.from_dict(net95_1.nodes, orient='index')
net95_1_NodeData
Out[5]:
votes weightCurrentGroup region counter experience experience_true votesPerc capital currentGroup party startGroup weightStartGroup label betweenness closeness eigenvector
00046218 7975.0 0.558 NA 12 0.0 0 0.002906 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 JHONG JUNCHAYA JUAN JOSE 0.008172 0.753165 0.132632
07238522 8304.0 0.142 NA 25 0.0 0 0.003026 0 UNION POR EL PERU UNION POR EL PERU UNION POR EL PERU 0.141667 GUERRA GARCIA CUEVA ROGER ANTENOR 0.009132 0.730061 0.120487
08201401 7791.0 0.058 NA 85 2.0 1 0.002839 0 COORDINADORA DEMOCRATICA-ACCION POPULAR ACCION POPULAR ACCION POPULAR 0.033333 ALVA ORLANDINI JAVIER ERNESTO 0.006847 0.721212 0.119679
05847587 8436.0 0.558 NA 23 0.0 0 0.003074 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 CAMPOS BACA LUIS ESEQUIEL 0.018071 0.793333 0.136519
32785850 6866.0 0.058 NA 50 1.0 1 0.002502 0 COORDINADORA DEMOCRATICA-ACCION POPULAR CODE - PAIS POSIBLE PAIS POSIBLE 0.041667 CHU RUBIO LUIS ALBERTO 0.011287 0.743750 0.122160
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
07586258 6204.0 0.050 NA 17 0.0 0 0.002261 0 GRUPO PARLAMENTARIO PERU MOVIMIENTO CIVICO NACIONAL OBRAS MOVIMIENTO CIVICO NACIONAL OBRAS 0.016667 LAJO LAZO MANUEL CONSTANTINO 0.009202 0.743750 0.125720
07194374 7354.0 0.558 NA 23 1.0 1 0.002680 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 AMURUZ GALLEGOS ROGER 0.003429 0.589109 0.048808
08772985 12158.0 0.050 NA 21 4.0 1 0.004430 0 PARTIDO POPULAR CRISTIANO-RENOVACION RENOVACION RENOVACION 0.025000 CHIRINOS SOTO ENRIQUE 0.000057 0.416084 0.001550
29233392 15353.0 0.558 NA 13 0.0 0 0.005595 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 FERNANDEZ BUSTINZA VICTOR RAUL 0.012056 0.762821 0.130147
17906056 11490.0 0.558 NA 23 2.0 1 0.004187 0 CAMBIO 90 CAMBIO 90 - NUEVA MAYORIA CAMBIO 90 0.558333 BARRETO ESTRADA GAMALIEL 0.002558 0.616580 0.076054

120 rows × 16 columns

We could use a scatter plot:

In [6]:
#updating altair in colab
# !pip install altair -U
In [7]:
import altair as alt

ALT_net=alt.Chart(net95_1_NodeData).properties(width=300,
                                               height=300)

ENC_net=ALT_net.encode(
    alt.X('betweenness'),
    alt.Y('closeness'),
    alt.Size("eigenvector:Q"),
    alt.Color("eigenvector:Q"),
    tooltip=['label','party']
).interactive()
ENC_net.mark_circle()
Out[7]:

Let me create add another attribute, to show belonging to a the governing party:

In [8]:
isPartyInGov={l:1 if p=='CAMBIO 90 - NUEVA MAYORIA' else 0 for (l,p) in nx.get_node_attributes(net95_1, 'party').items()}
nx.set_node_attributes(net95_1, isPartyInGov,'isPartyInGov')
net95_1_NodeData=pd.DataFrame.from_dict(net95_1.nodes, orient='index')
In [9]:
ALT_net=alt.Chart(net95_1_NodeData).properties(width=300,
                                               height=300)

ENC_net=ALT_net.encode(
    alt.X('betweenness'),
    alt.Y('closeness'),
    alt.Size("eigenvector:Q").scale(range=[10, 300]),
    alt.Color('isPartyInGov:N'),
    tooltip=['label','party']
).interactive()

ENC_net.mark_circle()
Out[9]:

Networks are complex to visualize, then some combination of plots may help. Let me introduce brushing:

In [10]:
brush = alt.selection_interval()

ENC_net_1= ALT_net.encode(alt.X('betweenness'),
               alt.Y('closeness'),
               alt.Size("eigenvector").scale(range=[10, 300]),
               alt.Color('isPartyInGov:N'),
               tooltip=['label','party']
              ).add_params(brush)

points = ENC_net_1.mark_circle()

ENC_net_2= ALT_net.encode(alt.Y('count()'),
                          alt.X('isPartyInGov:N'),
                          alt.Color('isPartyInGov:N')
                         ).transform_filter(brush)

bars = ENC_net_2.mark_bar()

points | bars
Out[10]:

Network as a whole
¶

The default network viz is not very promising in most cases:

In [11]:
nx.draw(net95_1)
No description has been provided for this image
In [12]:
# install nx_altair
# !pip install nx_altair
In [13]:
# install vegafusion
# !pip install "vegafusion[embed]>=1.5.0"
In [14]:
import matplotlib.pyplot as plt
import nx_altair as nxa
alt.data_transformers.enable("vegafusion")

pos=nx.kamada_kawai_layout(net95_1,scale=5)



chart = nxa.draw_networkx(
    G=net95_1,
    pos=pos,
    node_color='isPartyInGov:N',
    cmap='accent',
    edge_color='lightgrey'
)
chart
Out[14]:
In [15]:
# Get the node layer and edges
edges, nodes = chart.layer

# Build a brush
brush = alt.selection_interval()

# Condition nodes based on brush

nodes = nodes.encode(
    ).transform_filter(
    brush
).properties(
    width=500,
    height=600,
)

# bar shows selected nodes.
bars = alt.Chart(nodes.data).mark_bar().encode(
    alt.Y('count()',title='', scale=alt.Scale(domain=(0,120))),
    alt.X('party').sort('-y'),
    color=alt.Color('party', legend=None)
).add_params(
    brush
)

alt.hconcat(edges+nodes, bars)
Out[15]:

Looking for Communities
¶

We can not go very far with the previous plot; the next step is to find out if actually there are communities emerging from the relationships. Let's compute some basic net stats to see if we can suspect the existence of communities.

In [16]:
# (average) probability that two of your connections are also connected.
nx.transitivity(net95_1)
Out[16]:
0.6870013554096792
In [17]:
# (average) probability that all your connections are connected
nx.average_clustering(net95_1,count_zeros=False)
Out[17]:
0.6893858266780076
In [18]:
# the amout of maximal cliques
len(list(nx.find_cliques(net95_1)))
Out[18]:
1048
In [19]:
# The size of the maximal clique
maxsize_clique=max(len(c) for c in nx.find_cliques_recursive(net95_1))
maxsize_clique
Out[19]:
52

With this information, we can suspect nodes are organised into communities.

There are several algorithms for comunities detection. Let's use the Louvain algorithm:

In [20]:
# computing
legisLouvain=nx.community.louvain_communities(net95_1, seed=123)

# creating attribute
legisLouvain_attr={z:x for x,y  in enumerate(legisLouvain) for z in y }

# an attribute to the node
nx.set_node_attributes(net95_1, legisLouvain_attr,'louvain')

# how many?
print('comunities found:',len(legisLouvain))

comunities found: 6

The community label has been assigned to the nodes, let's recover the attributes as a data frame again:

In [21]:
net95_1_NodeData=pd.DataFrame.from_dict(net95_1.nodes, orient='index')
net95_1_NodeData.iloc[:,-5:]
Out[21]:
betweenness closeness eigenvector isPartyInGov louvain
00046218 0.008172 0.753165 0.132632 1 0
07238522 0.009132 0.730061 0.120487 0 5
08201401 0.006847 0.721212 0.119679 0 2
05847587 0.018071 0.793333 0.136519 1 0
32785850 0.011287 0.743750 0.122160 0 1
... ... ... ... ... ...
07586258 0.009202 0.743750 0.125720 0 2
07194374 0.003429 0.589109 0.048808 1 3
08772985 0.000057 0.416084 0.001550 0 3
29233392 0.012056 0.762821 0.130147 1 0
17906056 0.002558 0.616580 0.076054 1 0

120 rows × 5 columns

Let's create a viz:

In [22]:
# position of nodes
nodePos=nx.spring_layout(net95_1,k=0.5)

# drawing
chart = nxa.draw_networkx(G=net95_1,
                          pos=nodePos,
                          edge_color='grey',
                          width='weight',
                          alpha=0.6,
                          node_size='isPartyInGov:N',
                          node_color='louvain:N',
                          cmap='set1',
                          linewidths=0,
                          node_tooltip=['label','party'])
chart.properties(
    width=600,
    height=600,
).interactive()
Out[22]:

The library netgraph moves the nodes if you have communities:

In [23]:
# !pip install netgraph
In [24]:
# custom colors
community_to_color = {0 : 'blue', 1 : 'orange',2 : 'green',3 : 'white', 4:'black', 5:'magenta'}
# color dict
custom_node_color = {node: community_to_color[community_id] for node, community_id in legisLouvain_attr.items()}

from netgraph import Graph
Graph(net95_1,
      node_layout='community',edge_color='lightgrey',edge_alpha=0.3,edge_width=0.5,
      node_layout_kwargs=dict(node_to_community=legisLouvain_attr),
      node_color=custom_node_color)

/Users/JoseManuel/opt/anaconda3/envs/ASIES/lib/python3.11/site-packages/netgraph/_utils.py:360: RuntimeWarning: invalid value encountered in divide
  v = v / np.linalg.norm(v, axis=-1)[:, None] # unit vector
Out[24]:
<netgraph._main.Graph at 0x14796eb10>
No description has been provided for this image

You can try hives if we are intested in displaying interactions:

In [25]:
# !pip install hiveplotlib
In [26]:
from hiveplotlib import hive_plot_n_axes
from hiveplotlib.converters import networkx_to_nodes_edges
from hiveplotlib.node import split_nodes_on_variable
from hiveplotlib.viz import hive_plot_viz

# setup
## convert from networkx
nodes, edges = networkx_to_nodes_edges(net95_1)
## organize nodes into communities
communities_dict = split_nodes_on_variable(nodes, variable_name="louvain")
nodes_by_community_toAxes = list(communities_dict.values())
# amount of communities
amountOf_communities=len(nodes_by_community_toAxes)

Time to plot:

In [27]:
hp = hive_plot_n_axes(node_list=nodes,
                      edges=edges,
                      axes_assignments=nodes_by_community_toAxes,
                      sorting_variables=["eigenvector"] * amountOf_communities
)
fig, ax = hive_plot_viz(hp)
ax.set_title("Interaction among communities", y=1.05, size=20)
plt.show()
No description has been provided for this image

We could add some color:

In [28]:
fig, ax = hive_plot_viz(
    hp,
    node_kwargs={"color": "red", "s": 10},
    axes_kwargs={"color": "yellow"},
    color="grey",
    ls="dotted"
)
ax.set_title("Interaction among communities", y=1.05, size=20)
plt.show()
No description has been provided for this image

This library allows you to reveal within communities interaction:

In [29]:
hp = hive_plot_n_axes(node_list=nodes,
                      edges=edges,
                      axes_assignments=nodes_by_community_toAxes,
                      sorting_variables=["eigenvector"] * amountOf_communities,
                      repeat_axes=[True]*amountOf_communities,
                      all_edge_kwargs={"color": "darkgrey"},
                      repeat_edge_kwargs={"color": "magenta"})
fig, ax = hive_plot_viz(hp)
ax.set_title("Interaction between and within communities", y=1.05, size=20)
plt.show()

/var/folders/2n/bkfhfqq16r78g3hf7pdj56y40000gn/T/ipykernel_1481/617131583.py:1: UserWarning: Specified kwarg color in `all_edge_kwargs` but already set as kwarg for edge set index 0 with `edge_list_kwargs` or `repeat_edge_kwargs`. Disregarding `all_edge_kwargs` here.
  hp = hive_plot_n_axes(node_list=nodes,
/var/folders/2n/bkfhfqq16r78g3hf7pdj56y40000gn/T/ipykernel_1481/617131583.py:1: UserWarning: Specified kwarg color in `all_edge_kwargs` but already set as kwarg for edge set index 0 with `edge_list_kwargs` or `repeat_edge_kwargs`. Disregarding `all_edge_kwargs` here.
  hp = hive_plot_n_axes(node_list=nodes,
/var/folders/2n/bkfhfqq16r78g3hf7pdj56y40000gn/T/ipykernel_1481/617131583.py:1: UserWarning: Specified kwarg color in `all_edge_kwargs` but already set as kwarg for edge set index 0 with `edge_list_kwargs` or `repeat_edge_kwargs`. Disregarding `all_edge_kwargs` here.
  hp = hive_plot_n_axes(node_list=nodes,
/var/folders/2n/bkfhfqq16r78g3hf7pdj56y40000gn/T/ipykernel_1481/617131583.py:1: UserWarning: Specified kwarg color in `all_edge_kwargs` but already set as kwarg for edge set index 0 with `edge_list_kwargs` or `repeat_edge_kwargs`. Disregarding `all_edge_kwargs` here.
  hp = hive_plot_n_axes(node_list=nodes,
/var/folders/2n/bkfhfqq16r78g3hf7pdj56y40000gn/T/ipykernel_1481/617131583.py:1: UserWarning: Specified kwarg color in `all_edge_kwargs` but already set as kwarg for edge set index 0 with `edge_list_kwargs` or `repeat_edge_kwargs`. Disregarding `all_edge_kwargs` here.
  hp = hive_plot_n_axes(node_list=nodes,
/var/folders/2n/bkfhfqq16r78g3hf7pdj56y40000gn/T/ipykernel_1481/617131583.py:1: UserWarning: Specified kwarg color in `all_edge_kwargs` but already set as kwarg for edge set index 0 with `edge_list_kwargs` or `repeat_edge_kwargs`. Disregarding `all_edge_kwargs` here.
  hp = hive_plot_n_axes(node_list=nodes,
No description has been provided for this image

Finally, let's use heatmaps:

In [30]:
# import networkx as nx
# net95_1=nx.read_gexf("cospon_9596.gexf")
nodelist = list(net95_1.nodes)
A = nx.to_numpy_array(net95_1, nodelist=nodelist)
A
Out[30]:
array([[0., 2., 1., ..., 0., 3., 2.],
       [2., 0., 2., ..., 0., 1., 0.],
       [1., 2., 0., ..., 0., 1., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [3., 1., 1., ..., 0., 0., 0.],
       [2., 0., 0., ..., 0., 0., 0.]])

Let's use graspologic. Notice it requires a particular version of scipy.

In [31]:
# !pip install scipy==1.10.1
In [32]:
# !pip install graspologic

I recommend we restart the session after the last installations.

In [33]:
from graspologic.plot import heatmap

heatmap(A, cbar=True)
Out[33]:
<Axes: >
No description has been provided for this image

We can not say very much from that plot. But, let me recover the party of the legislators:

In [34]:
isPartyInGov_Values=[val for k,val in nx.get_node_attributes(net95_1, 'party').items()]
isPartyInGov_Values
Out[34]:
['CAMBIO 90 - NUEVA MAYORIA',
 'UNION POR EL PERU',
 'ACCION POPULAR',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CODE - PAIS POSIBLE',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'FRENTE INDEPENDIENTE MORALIZADOR',
 'CODE - PAIS POSIBLE',
 'CAMBIO 90 - NUEVA MAYORIA',
 'IZQUIERDA UNIDA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'FRENTE POPULAR AGRICOLA FIA DEL PERU',
 'RENOVACION',
 'CAMBIO 90 - NUEVA MAYORIA',
 'RENOVACION',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'FRENTE INDEPENDIENTE MORALIZADOR',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CODE - PAIS POSIBLE',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'FRENTE INDEPENDIENTE MORALIZADOR',
 'CAMBIO 90 - NUEVA MAYORIA',
 'PARTIDO APRISTA PERUANO',
 'PARTIDO APRISTA PERUANO',
 'UNION POR EL PERU',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'PARTIDO POPULAR CRISTIANO',
 'UNION POR EL PERU',
 'CAMBIO 90 - NUEVA MAYORIA',
 'PERU AL 2000 - FRENATRACA',
 'UNION POR EL PERU',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'PARTIDO APRISTA PERUANO',
 'UNION POR EL PERU',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'MOVIMIENTO CIVICO NACIONAL OBRAS',
 'ACCION POPULAR',
 'CAMBIO 90 - NUEVA MAYORIA',
 'UNION POR EL PERU',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'UNION POR EL PERU',
 'FRENTE INDEPENDIENTE MORALIZADOR',
 'PARTIDO APRISTA PERUANO',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CODE - PAIS POSIBLE',
 'UNION POR EL PERU',
 'UNION POR EL PERU',
 'UNION POR EL PERU',
 'PARTIDO APRISTA PERUANO',
 'UNION POR EL PERU',
 'ACCION POPULAR',
 'CAMBIO 90 - NUEVA MAYORIA',
 'PARTIDO POPULAR CRISTIANO',
 'CAMBIO 90 - NUEVA MAYORIA',
 'UNION POR EL PERU',
 'CAMBIO 90 - NUEVA MAYORIA',
 'UNION POR EL PERU',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'UNION POR EL PERU',
 'FRENTE INDEPENDIENTE MORALIZADOR',
 'PARTIDO APRISTA PERUANO',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'PARTIDO APRISTA PERUANO',
 'MOVIMIENTO INDEPENDIENTE AGRARIO',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'PARTIDO POPULAR CRISTIANO',
 'UNION POR EL PERU',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CODE - PAIS POSIBLE',
 'CAMBIO 90 - NUEVA MAYORIA',
 'ACCION POPULAR',
 'IZQUIERDA UNIDA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'FRENTE INDEPENDIENTE MORALIZADOR',
 'UNION POR EL PERU',
 'PARTIDO APRISTA PERUANO',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA',
 'UNION POR EL PERU',
 'MOVIMIENTO CIVICO NACIONAL OBRAS',
 'CAMBIO 90 - NUEVA MAYORIA',
 'RENOVACION',
 'CAMBIO 90 - NUEVA MAYORIA',
 'CAMBIO 90 - NUEVA MAYORIA']

Then, this nice heatplot appears:

In [35]:
heatmap(A, inner_hier_labels=isPartyInGov_Values, sort_nodes=True, cbar=False,hier_label_fontsize=4,transform='simple-all')
Out[35]:
<Axes: >
No description has been provided for this image

Finally, we could save the net with all the attributes added:

In [36]:
# nx.write_graphml(net95_1,'net95_1.graphml')